Abstract: We present a novel methodology based on Physics-Informed Neural Networks (PINNs) for solving systems of partial differential equations admitting discontinuous solutions. Our method, called Gradient-Annihilated PINNs (GA-PINNs), introduces a modified loss function that forces the model to partially ignore high-gradients in the physical variables, achieved by introducing a suitable weighting function. The method relies on a set of hyperparameters that control how gradients are treated in the physical loss. The performance of our methodology is demonstrated by solving Riemann problems in special relativistic hydrodynamics, extending earlier studies with PINNs in the context of the classical Euler equations. The solutions obtained with the GA-PINN model correctly describe the propagation speeds of discontinuities and sharply capture the associated jumps. We use the relative l(2) error to compare our results with the exact solution of special relativistic Riemann problems, used as the reference ''ground truth'', and with the corresponding error obtained with a second-order, central, shock-capturing scheme. In all problems investigated, the accuracy reached by the GA-PINN model is comparable to that obtained with a shock-capturing scheme, achieving a performance superior to that of the baseline PINN algorithm in general. An additional benefit worth stressing is that our PINN-based approach sidesteps the costly recovery of the primitive variables from the state vector of conserved variables, a well-known drawback of grid-based solutions of the relativistic hydrodynamics equations. Due to its inherent generality and its ability to handle steep gradients, the GA-PINN methodology discussed in this paper could be a valuable tool to model relativistic flows in astrophysics and particle physics, characterized by the prevalence of discontinuous solutions.

Abstract: A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale of evaporation neutrons. Additionally, it was observed to learn an energy-weighting indicative of longitudinal leakage correction. In addition, the method produced a linear detector response and outperformed a published control method regarding resolution for every particle energy studied.

Abstract: Current cosmological tensions show that it is crucial to test the predictions from the canonical ACDM paradigm at different cosmic times. One very appealing test of structure formation in the Universe is the growth rate of structure in our universe f, usually parametrized via the growth index gamma, with f equivalent to Omega(m)(a)gamma and gamma similar or equal to 0.55 in the standard ACDM case. Recent studies have claimed a suppression of the growth of structure from a variety of cosmological observations, characterized by gamma > 0.55. By employing different self-consistent growth parametrizations schemes, we show here that gamma < 0.55, obtaining instead an enhanced growth of structure today. This preference reaches the 3 sigma significance using cosmic microwave background observations, supernova Ia and baryon acoustic oscillation measurements. The addition of cosmic microwave background lensing data relaxes such a preference to the 2 sigma level, since a larger lensing effect can always be compensated with a smaller structure growth, or, equivalently, with gamma > 0.55. We have also included the lensing amplitude AL as a free parameter in our data analysis, showing that the preference for AL > 1 still remains, except for some particular parametrizations when lensing observations are included. We also do not find any significant preference for an oscillatory dependence of AL, AL + Am sin l. To further reassess the effects of a nonstandard growth, we have computed by means of N-body simulations the dark matter density fields, the dark matter halo mass functions and the halo density profiles for different values of gamma. Future observations from the Square Kilometer Array, reducing by a factor of 3 the current errors on the gamma parameter, further confirm or refute with a strong statistical significance the deviation of the growth index from its standard value.